Teaching Philosophy with Robots Creating a Lab Manual for Computational Modeling Autonomous Robots amp Embodied Cognition (PHIL 321)Claire Bartell 2016 Professor William Seeley Bates College Department of Philosophy

Abstract

Examining our conception of intelligence is relevant to many fields including neuroscience philosophy and computer science The class ldquoComputational Modeling Autonomous Robots amp Embodied Cognitionrdquo explores models in embodied cognition and artificial intelligence through simulations robotics and genetic algorithms By examining various topics students are encouraged to challenge their idea of intelligence During labs students program LEGO Mindstorms NXT robots using a C-based programming language (RobotC) to explore topics covered in class Creating a lab manual for this type of lab presents challenges such as finding the correct balance between enforcing concepts learned in class and leaving room for creative problem solving during the lab period

Didabots collectively create heaps of blocks in the center of the corral an emergent behavior using only a program to avoid obstacles Ants and other brood-sorting organisms demonstrate stigmergy where ldquothe worker does not direct his work but is guided by itrdquo (Holland and Melhuish 1999)

ANTI-SLAM robots replicate rotational errors exhibited by rats during the reorientation task and model the relative role of geometric and featural cues (eg light and color) as landmarks during navigation (rats confuse opposing corners of a rectangular enclosures when given solely geometric information Cheng 2008) Further studies would engage the use of cognitive maps as a means for navigation (Mataric 1991)

Braitenberg vehicles are the creation of Valentino Braitenberg a neuroscientist as a thought experiment to illustrate how a simple control structure can lead to the display of a wide variety of seemingly complex goal-oriented behaviors such as cowardliness and aggression All the robots in this course are based on a Braitenberg architecture These animats ldquosolverdquo two important problems in artificial intelligence the symbol grounding problemmdash where artificial systems have trouble attaching symbols to aspects of their surroundings and the frame problemmdashwhere the system cannot model the global effects of changes to its environment by renouncing the need for representation

Braitenberg Vehicles

Lemmings replicate the anular sorting behavior of brood-sorting ants and termites They leave white bricks against walls surrounding black bricks in the center of the corral Like Didabots Lemmings exhibit stigmergy collective intelligence and offer an opportunity to study morphological computation

Artificial Neural Networks (ANNs) offer a more representative model of natural intelligence than other more symbolic architectures They use nodes connections connection weights and activation levels to model neural output and propagation thus supporting learning and generalization Our robots learned to avoid walls by coupling sonar and collision information using an ANN and a Hebbian learning rule Δ (wij) = η ai aj mdash sometimes the programs also included a forgetting function ξ [(ai + aj) wij] They are examples of Distributed Adaptive Control that exploit connections between a proximity layer a collision layer and motor actions (Pfeifer and Scheier 1999)

Artificial Neural Networks

Navigationdistance (sonar) light

collision

Neural Architecture

motor action

collision layer

proximity layer

Left Turn

Right Turn

Touch 1 Touch 2Sonar 2Sonar 1

Prox 0 Prox 1

Coll 0 Coll 1

0 1 2 3

Heap FormationBoids model the flocking behavior of birds and fish Robots must maintain proper alignment cohesion and separation in order to flock effectively Flocking is achieved through local individualized strategies which result in global collective behavior

Our flockers are released to trail a ldquoline-followerrdquo robot and have sported a number of different body types and brain architectures

motors

ldquobrickrdquo

Flocking

Sorting

Many thanks to William Seeley The Bates Philosophy Department the students in

PHIL321 and Matt Duvall in the Bates College Imaging and Computing Center

Acknowledgments

Braitenberg V (1986) Vehicles Experiments in Synthetic Psychology Cambridge The MIT Press Cheng K (2008) Whither geometry Troubles of the geometric module Trends in Cognitive Science 12(9) 355-361 Holland O amp Melhuish C (1999) Stigmergy Self-Organization and Sorting in Collective Robotics Artificial Life 5(2) 173-202

Mataric M J (1991) Navigating With a Rat Brain A Neurobiologically-Inspired Model for Robot Spatial Representation In J Meyer amp S Wilson (Eds) From Animals to Animats Proceedings of the First International Conference on Simulation of Adpative Behavior (SAB-90) (pp 169-175) Cambridge MIT Press Pfeifer R amp Scheier C (1999) Understanding Intelligence Cambridge MIT Press

References

Coward Aggressive

Figure adapted from Braitenberg 1986 p 8

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0 5 10 17 22 29 35 43

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Time (sec)

Sensor Array

Proximity-Collision Weights